Multimodal Encoder Filters Image Tags
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image tagging systems face challenges in generating accurate tags due to noisy user-provided metadata, which can include irrelevant tags, making it difficult to train effective machine learning models for image processing tasks such as image retrieval and generation.
Innovation Solution
An image processing apparatus filters tags based on semantic similarities and user-interaction data to curate a clean dataset for training image tagging models, using a multimodal encoder to generate image and text embeddings and behavioral filtering to prioritize relevant tags, thereby improving the accuracy of image tagging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If user-provided metadata is used for image tagging, then the quantity of training data is increased, but the accuracy of tags deteriorates due to noisy and irrelevant tags
Solution Approach 1:
The patent extracts and separates relevant image-tag pairs from irrelevant ones by filtering out noisy user-provided metadata. The system identifies and removes tags that are semantically unrelated to the image content, thereby obtaining a cleaner training dataset that maintains both quantity and quality of useful training examples.
Solution Approach 2:
The patent introduces an intermediary filtering mechanism that acts as a mediator between raw user-provided metadata and the training data. This intermediary component evaluates semantic relevance and user interaction patterns to selectively retain only the most relevant tags, thus bridging the gap between large quantity of data and high accuracy requirements.
2Measurement precision
If semantic similarity filtering is applied to tags, then the accuracy of image tagging is improved, but the complexity of the processing system increases
Solution Approach 1:
The patent employs a multimodal encoder that serves multiple functions: it encodes both images and text, computes semantic similarities, and generates embeddings for filtering. This multi-functional component reduces overall system complexity by consolidating multiple operations into a single unified model rather than requiring separate specialized components for each task.
Solution Approach 2:
The patent changes the parameter space by transforming tags into embedding vectors through the multimodal encoder. This parameter transformation enables efficient semantic similarity computation and filtering operations, making the complex filtering process computationally feasible and reducing practical system complexity through optimized mathematical representations.
3Measurement precision
If human-labeled datasets are used for training, then the accuracy of the model is improved, but the cost and time required for data preparation increases
Solution Approach 1:
The patent enables the system to self-service by automatically filtering and curating training data using semantic similarity metrics and user interaction patterns. Instead of requiring manual human annotation, the system autonomously evaluates and selects relevant image-tag pairs, significantly reducing the time and resources needed for data preparation while maintaining high training data quality.
Solution Approach 2:
The patent replaces expensive and time-consuming human labeling with automated filtering mechanisms that use computational resources. The system employs disposable automated filters that can rapidly process large datasets without the need for costly human annotators, achieving cost-effective data preparation that scales efficiently.
Data Source
AI summary
Systems and methods for image tagging are provided. One aspect of the systems and methods includes encoding an image and a tag of the image using a multimodal encoder to obtain an image embedding and a text embedding, respectively. Another aspect of the systems and methods includes generating training data for a machine learning model by filtering a plurality of image-tag pairs based on a similarity between the image embedding and the text embedding. Another aspect of the systems and methods includes training the machine learning model using the training data.